nl.uva.sne.classifiers.Hierarchical.java Source code

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Here is the source code for nl.uva.sne.classifiers.Hierarchical.java

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package nl.uva.sne.classifiers;

import java.awt.Container;
import java.awt.GridLayout;
import java.io.File;
import java.io.IOException;
import java.util.Map;
import java.util.Properties;
import java.util.logging.Level;
import java.util.logging.Logger;
import javax.swing.JFrame;
import nl.uva.sne.commons.ClusterUtils;
import org.json.simple.parser.ParseException;

import weka.clusterers.HierarchicalClusterer;
import weka.core.ChebyshevDistance;
import weka.core.DistanceFunction;
import weka.core.EuclideanDistance;
import weka.core.Instances;
import weka.core.ManhattanDistance;
import weka.core.MinkowskiDistance;
import weka.core.converters.ArffSaver;
import weka.filters.unsupervised.attribute.Remove;
import weka.gui.hierarchyvisualizer.HierarchyVisualizer;

/**
 *
 * @author S. Koulouzis
 */
public class Hierarchical implements Clusterer {

    private int numOfClusters;
    private String distanceFunction;

    @Override
    public void configure(Properties properties) {
        numOfClusters = Integer.valueOf(properties.getProperty("kmeans.num.of.clusters", "6"));
        distanceFunction = properties.getProperty("distance.function", "Euclidean");
        Logger.getLogger(Hierarchical.class.getName()).log(Level.INFO, "kmeans.num.of.clusters: {0}",
                numOfClusters);
    }

    @Override
    public Map<String, String> cluster(String inDir) throws IOException, ParseException {
        try {

            Instances data = ClusterUtils.terms2Instances(inDir, false);

            //            ArffSaver s = new ArffSaver();
            //            s.setInstances(data);
            //            s.setFile(new File(inDir+"/dataset.arff"));
            //            s.writeBatch();

            DistanceFunction df;
            //            SimpleKMeans currently only supports the Euclidean and Manhattan distances.
            switch (distanceFunction) {
            case "Minkowski":
                df = new MinkowskiDistance(data);
                break;
            case "Euclidean":
                df = new EuclideanDistance(data);
                break;
            case "Chebyshev":
                df = new ChebyshevDistance(data);
                break;
            case "Manhattan":
                df = new ManhattanDistance(data);
                break;
            default:
                df = new EuclideanDistance(data);
                break;
            }

            Logger.getLogger(Hierarchical.class.getName()).log(Level.INFO, "Start clusteing");

            weka.clusterers.HierarchicalClusterer clusterer = new HierarchicalClusterer();
            clusterer.setOptions(new String[] { "-L", "COMPLETE" });
            clusterer.setDebug(true);
            clusterer.setNumClusters(numOfClusters);
            clusterer.setDistanceFunction(df);
            clusterer.setDistanceIsBranchLength(true);
            clusterer.setPrintNewick(false);

            weka.clusterers.FilteredClusterer fc = new weka.clusterers.FilteredClusterer();
            String[] options = new String[2];
            options[0] = "-R"; // "range"
            options[1] = "1"; // we want to ignore the attribute that is in the position '1'
            Remove remove = new Remove(); // new instance of filter
            remove.setOptions(options); // set options

            fc.setFilter(remove); //add filter to remove attributes
            fc.setClusterer(clusterer); //bind FilteredClusterer to original clusterer
            fc.buildClusterer(data);

            //             // Print normal
            //        clusterer.setPrintNewick(false);
            //        System.out.println(clusterer.graph());
            //        // Print Newick
            //        clusterer.setPrintNewick(true);
            //        System.out.println(clusterer.graph());
            //
            //        // Let's try to show this clustered data!
            //        JFrame mainFrame = new JFrame("Weka Test");
            //        mainFrame.setSize(600, 400);
            //        mainFrame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
            //        Container content = mainFrame.getContentPane();
            //        content.setLayout(new GridLayout(1, 1));
            //
            //        HierarchyVisualizer visualizer = new HierarchyVisualizer(clusterer.graph());
            //        content.add(visualizer);
            //
            //        mainFrame.setVisible(true);
            return ClusterUtils.bulidClusters(clusterer, data, inDir);

        } catch (Exception ex) {
            Logger.getLogger(Hierarchical.class.getName()).log(Level.SEVERE, null, ex);
        }
        return null;
    }

}